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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Assessment of APCCmultimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983-2003) and real-time forecasts (2008-2013)
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Assessment of APCCmultimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983-2003) and real-time forecasts (2008-2013)

机译:在季节气候预测中对APCC多模型集合预测的评估:回顾(1983-2003)和实时预测(2008-2013)

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摘要

Since 2007, the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective (1983-2003) and real-time (2008-2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single-model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression-based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.
机译:自2007年以来,亚太经济合作组织(APEC)气候中心(APCC)每月发布3个月的多模型合奏(MME)季节预测,提前期为1个月,并分发给APEC成员经济体。本文提供了有关APCC多模型运行状况的全面文档,其中包括大量的回顾性(1983-2003)和实时(2008-2013)温度和降水预测。为了研究在APCC上实施的经验加权MME(使用多元回归)和校准MME(通过使用逐步模式投影方法校正单模型预测)方案可以实现的季节性可预测性的增强,我们将它们进行了比较使用简单的平均MME(具有相等的权重)进行预测,以预测1个月前的季节平均温度和降水。结果表明,当考虑运营预测系统的各个方面的预测时(即,在不同的变量,区域和季节中),简单的平均MME始终胜过基于多元回归的MME,而经过校准的MME显示出减少错误的能力并提高了大部分案例的预测技巧。讨论了APCC操作中实现的不同MME方法失败和成功的可能原因。

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